Abstract
Nowadays, Internet of Things (IoT) based applications are widely used in different sectors because of their high mobility, low cost, and efficiency. However, the wide usage of these applications leads to various security issues. Several security applications exist for protecting multimedia data, but the appropriate confidential range is not met due to the multi-variant features. Hence, the novel hybrid Elman Neural-based Blowfish Blockchain Model has been developed in this article to secure IoT healthcare multimedia data. Here, the Elman network features provided continuous monitoring for predicting malicious events in the trained multimedia data. In addition, the crypto analysis was performed to enhance the confidentiality rate by hiding the raw data from third parties. The presented model was verified using python software. Furthermore, the robustness of the developed model is validated with a crypt analysis by launching attacks. Finally, the outcomes were estimated and compared with the existing techniques in terms of Encryption time, decryption time, execution time, error rate and confidential rate. Here, the evaluation database is the multimedia data, which is high in data size. Henceforth, the performance of the security model for securing multimedia data depends on time. Considering this, the time evaluation is measured in three classes: encryption, decryption and execution. The comparative analysis proves that the developed model achieved better results than others.
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References
Nasr Esfahani, M., Shahgholi Ghahfarokhi, B., & Etemadi Borujeni, S. (2021). End-to-end privacy preserving scheme for IoT-based healthcare systems. Wireless Networks, 27, 4009–4037. https://doi.org/10.1007/s11276-021-02652-9
Mousavi, S. K., Ghaffari, A., Besharat, S., & Afshari, H. (2021). Security of internet of things based on cryptographic algorithms: A survey. Wireless Networks, 27, 1515–1555. https://doi.org/10.1007/s11276-020-02535-5
Kore, A., & Patil, S. (2022). Cross layered cryptography based secure routing for IoT-enabled smart healthcare system. Wireless Networks, 28, 287–301. https://doi.org/10.1007/s11276-021-02850-5
Othman, S. B., Almalki, F. A., Chakraborty, C., & Sakli, H. (2022). Privacy-preserving aware data aggregation for IoT-based healthcare with green computing technologies. Computers and Electrical Engineering, 101, 108025. https://doi.org/10.1016/j.compeleceng.2022.108025
Masud, M., Gaba, G. S., Choudhary, K., Alroobaea, R., & Shamim Hossain, M. (2021). A robust and lightweight secure access scheme for cloud based E-healthcare services. Peer-to-peer Networking and Applications, 14(5), 3043–3057. https://doi.org/10.1007/s12083-021-01162-x
Majeed, U., Khan, L. U., Yaqoob, I., Kazmi, S. M. A., Salah, K., & Hong, C. S. (2021). Blockchain for IoT-based smart cities: Recent advances, requirements, and future challenges. Journal of Network and Computer Applications, 181, 103007. https://doi.org/10.1016/j.jnca.2021.103007
Saha, R., Kumar, G., Devgun, T., Buchanan, W., Thomas, R., Alazab, M., Kim, T. H., & Rodrigues, J. (2021). A blockchain framework in post-quantum decentralization. IEEE Transactions on Services Computing, 16(1), 1–12. https://doi.org/10.1109/TSC.2021.3116896
Cherukupalli, N. L. S., & Katneni, V. (2021). Hiding data by combining AES cryptography with coverless image steganography using DCGAN: A review. In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE. https://doi.org/10.1109/ICECA52323.2021.9675966
Papaioannou, T. G., Stankovski, V., Kochovski, P., Simonet-Boulogne, A., Barelle, C., Ciaramella, A., Ciaramella, M., & Stamoulis, G. D. (2021). A new blockchain ecosystem for trusted, traceable and transparent ontological knowledge management. In International Conference on the Economics of Grids, Clouds, Systems, and Services, Springer, Cham. https://doi.org/10.1007/978-3-030-92916-9_8
Kumar, R., & Sharma, R. (2021). Leveraging blockchain for ensuring trust in IoT: A survey. Journal of King Saud University-Computer and Information Sciences, 34(10), 8599–8622. https://doi.org/10.1016/j.jksuci.2021.09.004
Haddad, A., Habaebi, M. H., Islam, M. R., Hasbullah, N. F., & Zabidi, S. A. (2022). Systematic review on AI-Blockchain based E-Healthcare records management systems. IEEE Access, 10, 94583–94615. https://doi.org/10.1109/ACCESS.2022.3201878
Mangla, S. K., Kazancoglu, Y., Ekinci, E., Liu, M., Özbiltekin, M., & Sezer, M. D. (2021). Using system dynamics to analyze the societal impacts of blockchain technology in milk supply chainsrefer. Transportation Research Part E: Logistics and Transportation Review, 149, 102289. https://doi.org/10.1016/j.tre.2021.102289
Saygili, M., Mert, I. E., & Tokdemir, O. B. (2022). A decentralized structure to reduce and resolve construction disputes in a hybrid blockchain network. Automation in construction, 134, 104056. https://doi.org/10.1016/j.autcon.2021.104056
Tanveer, M., Rajani, T., Rastogi, R., Shao, Y. H., & Ganaie, M. A. (2022). Comprehensive review on twin support vector machines. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04575-w
Antoniadis, A., Lambert-Lacroix, S., & Poggi, J. M. (2021). Random forests for global sensitivity analysis: A selective review. Reliability Engineering & System Safety, 206, 107312. https://doi.org/10.1016/j.ress.2020.107312
Ab Aziz, M. F., Mostafa, S. A., Foozy, C. F. M., Mohammed, M. A., Elhoseny, M., & Abualkishik, A. Z. (2021). Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Systems with Applications, 183, 115441. https://doi.org/10.1016/j.eswa.2021.115441
Prabadevi, B., Deepa, N., Pham, Q. V., Nguyen, D. C., Praveen Kumar, R. M., Thippa, R. G., Pathirana, P. N., & Dobre, O. A. (2021). Toward blockchain for edge-of-things: A new paradigm, opportunities, and future directions. IEEE Internet of Things Magazine, 4(2), 102–108. https://doi.org/10.1109/IOTM.0001.2000191
Latif, S., Idrees, Z., & e Huma, Z., & Ahmad, J. (2021). Blockchain technology for the industrial Internet of Things: A comprehensive survey on security challenges, architectures, applications, and future research directions. Transactions on Emerging Telecommunications Technologies, 32(11), e4337. https://doi.org/10.1002/ett.4337
Sharma, P., Namasudra, S., Crespo, R. G., Parra-Fuente, J., & Trivedi, M. C. (2023). EHDHE: Enhancing security of healthcare documents in IoT-enabled digital healthcare ecosystems using blockchain. Information Sciences, 629, 703–718. https://doi.org/10.1016/j.ins.2023.01.148
Nanda, S. K., Panda, S. K., & Dash, M. (2023). Medical supply chain integrated with blockchain and IoT to track the logistics of medical products. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-14846-8
Abikoye, O. C., Oladipupo, E. T., Imoize, A. L., Awotunde, J. B., Lee, C. C., & Li, C. T. (2023). Securing critical user information over the internet of medical things platforms using a hybrid cryptography scheme. Future Internet, 15(3), 99. https://doi.org/10.3390/fi15030099
Taloba, A. I., Elhadad, A., Rayan, A., Abd El-Aziz, R. M., Salem, M., Alzahrani, A. A., Alharithi, F. S., & Park, C. (2023). A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare. Alexandria Engineering Journal, 65, 263–274. https://doi.org/10.1016/j.aej.2022.09.031
Zhao, Z., Li, X., Luan, B., Jiang, W., Gao, W., & Neelakandan, S. (2023). Secure internet of things (IoT) using a novel brooks iyengar quantum byzantine agreement-centered blockchain networking (BIQBA-BCN) model in smart healthcare. Information Sciences, 629, 440–455. https://doi.org/10.1016/j.ins.2023.01.020
Xu, L., Yu, X., & Gulliver, T. A. (2021). Intelligent outage probability prediction for mobile IoT networks based on an IGWO-elman neural network. IEEE Transactions on Vehicular Technology, 70(2), 1365–1375. https://doi.org/10.1109/TVT.2021.3051966
Sharma, S., Patel, K. N., & Jha, A. S. (2021). Cryptography using blowfish algorithm. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE. https://doi.org/10.1109/ICAC3N53548.2021.9725661
Velmurugadass, P., Dhanasekaran, S., Anand, S. S., & Vasudevan, V. (2021). Enhancing Blockchain security in cloud computing with IoT environment using ECIES and cryptography hash algorithm. Materials Today: Proceedings, 37, 2653–2659. https://doi.org/10.1016/j.matpr.2020.08.519
Pourvahab, M., & Ekbatanifard, G. (2019). Digital forensics architecture for evidence collection and provenance preservation in iaas cloud environment using sdn and blockchain technology. IEEE Access, 7, 153349–153364. https://doi.org/10.1109/ACCESS.2019.2946978
Li, H., & Han, D. (2019). EduRSS: A blockchain-based educational records secure storage and sharing scheme. IEEE Access, 7, 179273–179289. https://doi.org/10.1109/ACCESS.2019.2956157
Zhao, X., Huang, G., Jiang, J., Gao, L., & Li, M. (2021). Research on lightweight anomaly detection of multimedia traffic in edge computing. Computers & Security, 111, 102463. https://doi.org/10.1016/j.cose.2021.102463
Dhar, S., Khare, A., & Singh, R. (2022). Advanced security model for multimedia data sharing in Internet of Things. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.4621
Rajavel, R., Ravichandran, S. K., Harimoorthy, K., Nagappan, P., & Gobichettipalayam, K. R. (2022). IoT-based smart healthcare video surveillance system using edge computing. Journal of Ambient Intelligence and Humanized Computing, 13, 3195–3207. https://doi.org/10.1007/s12652-021-03157-1
Rajavel, R., Sundaramoorthy, B., Kanagachidambaresan, G. R., Ravichandran, S. K., & Leelasankar, K. (2022). Cloud-enabled diabetic retinopathy prediction system using optimized deep belief network classifier. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-04114-2
Kumari, A., Pranav, P., Dutta, S., & Chakraborty, S. (2023). Empirical and Statistical Comparison of RSA and El-Gamal in Terms of Time Complexity. Intelligent Cyber Physical Systems and Internet of Things: ICoICI 2022 (pp. 111–120). Springer International Publishing. https://doi.org/10.1007/978-3-031-18497-0_9
Ramachandra, M. N., Srinivasa Rao, M., Lai, W. C., Parameshachari, B. D., Babu, J. A., & Hemalatha, K. L. (2022). An efficient and secure big data storage in cloud environment by using triple data encryption standard. Big Data and Cognitive Computing, 6(4), 101. https://doi.org/10.3390/bdcc6040101
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Karthik, G.M., Kalyana Kumar, A.S., Karri, A.B. et al. Deep intelligent blockchain technology for securing IoT-based healthcare multimedia data. Wireless Netw 29, 2481–2493 (2023). https://doi.org/10.1007/s11276-023-03333-5
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DOI: https://doi.org/10.1007/s11276-023-03333-5